Model robust profile monitoring for the generalized linear mixed model for Phase I analysis
<p> The generalized linear mixed model (GLMM) becomes very popular in profile monitoring, especially when the production processes follow nonnormal distribution. In most of the real-life applications in industry, medicine, biology… and so on researchers assume that the response variable follow...
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2023
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| _version_ | 1864513566128734208 |
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| author | Keerthi Bandara (14777077) |
| author2 | Abdel‐Salam G. Abdel‐Salam (14777080) Jeffrey B. Birch (14777083) |
| author2_role | author author |
| author_facet | Keerthi Bandara (14777077) Abdel‐Salam G. Abdel‐Salam (14777080) Jeffrey B. Birch (14777083) |
| author_role | author |
| dc.creator.none.fl_str_mv | Keerthi Bandara (14777077) Abdel‐Salam G. Abdel‐Salam (14777080) Jeffrey B. Birch (14777083) |
| dc.date.none.fl_str_mv | 2023-03-16T06:18:42Z |
| dc.identifier.none.fl_str_mv | 10.1002/asmb.2587 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Model_robust_profile_monitoring_for_the_generalized_linear_mixed_model_for_Phase_I_analysis/22257529 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Human society Policy and administration Management Science and Operations Research General Business, Management and Accounting Modeling and Simulation |
| dc.title.none.fl_str_mv | Model robust profile monitoring for the generalized linear mixed model for Phase I analysis |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p> The generalized linear mixed model (GLMM) becomes very popular in profile monitoring, especially when the production processes follow nonnormal distribution. In most of the real-life applications in industry, medicine, biology… and so on researchers assume that the response variable follows a Bernoulli or Binomial distribution. The majority of previous studies in profile monitoring focused on parametric modeling using the logistic regression model, with both fixed or random effects, under the assumption of correct model specification. This research considers those cases where the parametric logistic regression model for the family of profiles is unknown or at least uncertain. Consequently, we propose two mixed model methods to monitor profiles from the exponential family: a nonparametric (NP) regression method based on the penalized spline regression technique and a semiparametric method (model robust profile monitoring for the generalized linear mixed model) which combines the advantages of both the parametric and NP methods. Several Hotelling T2 charts that have been studied for a binary response variable with replicates for Phase I profile monitoring. The performance of the proposed method is evaluated by using mean squares of errors and probability of signals criteria. The results showed satisfactory performance of the proposed control charts. </p> <h2>Other Information</h2> <p>Published in: Applied Stochastic Models in Business and Industry<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br> See article on publisher's website: <a href="http://dx.doi.org/10.1002/asmb.2587" target="_blank">http://dx.doi.org/10.1002/asmb.2587</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_b4aedef24ac7de0a5fae3ac5fb8233ea |
| identifier_str_mv | 10.1002/asmb.2587 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/22257529 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Model robust profile monitoring for the generalized linear mixed model for Phase I analysisKeerthi Bandara (14777077)Abdel‐Salam G. Abdel‐Salam (14777080)Jeffrey B. Birch (14777083)Human societyPolicy and administrationManagement Science and Operations ResearchGeneral Business, Management and AccountingModeling and Simulation<p> The generalized linear mixed model (GLMM) becomes very popular in profile monitoring, especially when the production processes follow nonnormal distribution. In most of the real-life applications in industry, medicine, biology… and so on researchers assume that the response variable follows a Bernoulli or Binomial distribution. The majority of previous studies in profile monitoring focused on parametric modeling using the logistic regression model, with both fixed or random effects, under the assumption of correct model specification. This research considers those cases where the parametric logistic regression model for the family of profiles is unknown or at least uncertain. Consequently, we propose two mixed model methods to monitor profiles from the exponential family: a nonparametric (NP) regression method based on the penalized spline regression technique and a semiparametric method (model robust profile monitoring for the generalized linear mixed model) which combines the advantages of both the parametric and NP methods. Several Hotelling T2 charts that have been studied for a binary response variable with replicates for Phase I profile monitoring. The performance of the proposed method is evaluated by using mean squares of errors and probability of signals criteria. The results showed satisfactory performance of the proposed control charts. </p> <h2>Other Information</h2> <p>Published in: Applied Stochastic Models in Business and Industry<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br> See article on publisher's website: <a href="http://dx.doi.org/10.1002/asmb.2587" target="_blank">http://dx.doi.org/10.1002/asmb.2587</a></p>2023-03-16T06:18:42ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1002/asmb.2587https://figshare.com/articles/journal_contribution/Model_robust_profile_monitoring_for_the_generalized_linear_mixed_model_for_Phase_I_analysis/22257529CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/222575292023-03-16T06:18:42Z |
| spellingShingle | Model robust profile monitoring for the generalized linear mixed model for Phase I analysis Keerthi Bandara (14777077) Human society Policy and administration Management Science and Operations Research General Business, Management and Accounting Modeling and Simulation |
| status_str | publishedVersion |
| title | Model robust profile monitoring for the generalized linear mixed model for Phase I analysis |
| title_full | Model robust profile monitoring for the generalized linear mixed model for Phase I analysis |
| title_fullStr | Model robust profile monitoring for the generalized linear mixed model for Phase I analysis |
| title_full_unstemmed | Model robust profile monitoring for the generalized linear mixed model for Phase I analysis |
| title_short | Model robust profile monitoring for the generalized linear mixed model for Phase I analysis |
| title_sort | Model robust profile monitoring for the generalized linear mixed model for Phase I analysis |
| topic | Human society Policy and administration Management Science and Operations Research General Business, Management and Accounting Modeling and Simulation |